10
Views
1
CrossRef citations to date
0
Altmetric
Original Research

Assessing 3D scores for protein structure fragment mining

&
Pages 67-77 | Published online: 14 Jul 2010

References

  • Holm L, Sander C. The FSSP database of structurally aligned protein fold families. Nucleic Acids Res. 1994;22:3600–3609.
  • Murzin AG, Brenner SE, Hubbard T, Chothia C. SCOP: a structural classification of proteins database for the investigation of sequences and structures. JMol Biol. 1995;247:536–540.
  • Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH - a hierarchic classification of protein domain structures. Structure. 1997;5:1093–1108.
  • Brenner SE, Koehl P, Levitt M. The ASTRAL compendium for protein structure and sequence analysis. Nucleic Acids Res. 2000;28:254–256.
  • Sam V, Tai CH, Garnier J, Gibrat JF, Lee B, Munson PJ. Towards an automatic classification of protein structural domains based on structural similarity. BMC Bioinformatics. 2008;9:74.
  • Madej T, Gibrat JF, Bryant SH. Threading a database of protein cores. Proteins. 1995;23:356–369.
  • Sanchez R, Sali A. Advances in comparative protein-structure modelling. Curr Opin Struct Biol. 1997;7:206–214.
  • Zemla A, Venclovas C, Moult J, Fidelis K. Processing and analysis of CASP3 protein structure predictions. Proteins. 1999;Suppl 3:22–29.
  • Siew N, Elofsson A, Rychlewski L, Fischer D. MaxSub: an automated measure for the assessment of protein structure prediction quality. Bioinformatics. 2000;16:776–785.
  • Sadreyev RI, Shi S, Baker D, Grishin NV Structure similarity measure with penalty for close non-equivalent residues. Bioinformatics. 2009;25:1259–1263.
  • Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Proteins. 2004;57:702–710.
  • Whisstock JC, Lesk AM. Prediction of protein function from protein sequence and structure. QRevBiophys. 2003;36:307–340.
  • Shah PK, Aloy P, Bork P, Russell RB. Structural similarity to bridge sequence space: finding new families on the bridges. Protein Sci. 2005;14:1305–1314.
  • Shindyalov IN, Bourne PE. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng. 1998;11:739–747.
  • Ortiz AR, Strauss CEM, Olmea O. MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison. Protein Sci. 2002;11:2606–2621.
  • Zemla A. LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res. 2003;31:3370–3374.
  • Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005;33:2302–2309.
  • Pandit SB, Skolnick J. Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score. BMC Bioinformatics. 2008;9:531.
  • Mizuguchi K, Go N. Comparison of spatial arrangements of secondary structural elements in proteins. Protein Eng. 1995;8:353–362.
  • Akutsu T, Onizuka K, Ishikawa M. Rapid protein fragment search using hash functions based on the Fourier transform. Comput Appl Biosci. 1997;13:357–364.
  • Samson AO, Levitt M. Protein segment finder: an online search engine for segment motifs in the PDB. Nucleic Acids Res. 2009;37:D224-D228.
  • Fischer D, Bachar O, Nussinov R, Wolfson H. An efficient automated computer vision based technique for detection of three dimensional structural motifs in proteins. J Biomol Struct Dyn. 1992;9:769–789.
  • Escalier V Pothier J, Soldano H, Viari A. Pairwise and multiple identification of three-dimensional common substructures in proteins. J Comput Biol. 1998;5:41–56.
  • Stark A, Sunyaev S, Russell RB. A model for statistical significance of local similarities in structure. J Mol Biol. 2003;326:1307–1316.
  • Brakoulias A, Jackson RM. Towards a structural classification of phosphate binding sites in protein-nucleotide complexes: an automated all-against-all structural comparison using geometric matching. Proteins. 2004;56:250–260.
  • Nebel JC, Herzyk P, Gilbert DR. Automatic generation of 3D motifs for classification of protein binding sites. BMC Bioinformatics. 2007;8:321.
  • Jakuschev S, Hoffman D. A novel algorithm for macromolecular epitope matching. Algorithms. 2009;2:498–517.
  • Sommer I, Muller O, Domingues FS, Sander O, Weickert J, Lengauer T. Moment invariants as shape recognition technique for comparing protein binding sites. Bioinformatics. 2007;23:3139–3146.
  • Sael L, Li B, La D, et al. Fast protein tertiary structure retrieval based on global surface shape similarity. Proteins. 2008;72:1259–1273.
  • Bystroff C, Baker D. Prediction of local structure in proteins using a library of sequence-structure motifs. J Mol Biol. 1998;281:565–577.
  • Camproux AC, Gautier R, Tuffery P. A hidden markov model derived structural alphabet for proteins. J Mol Biol. 2004;339:591–605.
  • Bujnicki JM. Protein-structure prediction by recombination of fragments. Chembiochem. 2006;7:19–27.
  • Weinhold N, Sander O, Domingues FS, Lengauer T, Sommer I. Local function conservation in sequence and structure space. PLoS Comput Biol. 2008;4:e1000105.
  • Mizuguchi K, Go N. Seeking significance in three-dimensional protein structure comparisons. Curr Opin Struct Biol. 1995;5:377–382.
  • Maiorov VN, Crippen GM. Size-independent comparison of protein three-dimensional structures. Proteins. 1995;22:273–283.
  • Carugo O, Pongor S. A normalized root-mean-square distance for comparing protein three-dimensional structures. Protein Sci. 2001;10:1470–1473.
  • Betancourt MR, Skolnick J. Universal similarity measure for comparing protein structures. Biopolymers. 2001;59:305–309.
  • Prasad JC, Comeau SR, Vajda S, Camacho CJ. Consensus alignment for reliable framework prediction in homology modeling. Bioinformatics. 2003;19:1682–1691.
  • Aloy P, Stark A, Hadley C, Russell RB. Predictions without templates: new folds, secondary structure, and contacts in CASP5. Proteins. 2003;53 Suppl 6:436–456.
  • Levitt M. A simplified representation of protein conformations for rapid simulation of protein folding. J Mol Biol. 1976;104:59–107.
  • Holm L, Sander C. Protein structure comparison by alignment of distance matrices. J Mol Biol. 1993;233:123–138.
  • Holm L, Sander C. Dali: a network tool for protein structure comparison. Trends Biochem Sci. 1995;20:478–480.
  • Maiorov VN, Crippen GM. Significance of root-mean-square deviation in comparing three-dimensional structures of globular proteins. J Mol Biol. 1994;235:625–634.
  • Levitt M, Gerstein M. A unified statistical framework for sequence comparison and structure comparison. Proc Natl Acad Sci U S A. 1998;95:5913–5920.
  • Wrabl JO, Grishin NV. Statistics of random protein superpositions: P-val- ues for pairwise structure alignment. J Comput Biol. 2009;15:317–355.
  • Lindahl E, Elofsson A. Identification of related proteins on family, superfamily and fold level. J Mol Biol. 2000;295:613–625.
  • McLachlan AD. A mathematical procedure for superimposing atomic coordinates of proteins. Acta Crystallogr A. 1972;A28:656–657.
  • Kabsch W. A solution for the best rotation to relate two sets of vecctors. Acta Crystallogr A. 1976;32:922–923.
  • Kabsch W. A discussion of the solution for the best rotation to relate two sets of vectors. Acta Crystallogr A. 1978;34:827–828.
  • Sippl MJ. On the problem of comparing protein structures. Development and applications of a new method for the assessment of structural similarities of polypeptide conformations. J Mol Biol. 1982;156:359–388.
  • Lesk AM, Levitt M, Chothia C. Alignment of the amino acid sequences of distantly related proteins using variable gap penalties. Protein Eng. 1986;1:77–78.
  • Zuker M, Somorjai RL. The alignment of protein structures in three dimensions. Bull Math Biol. 1989;51:55–78.
  • Feng ZK, Sippl MJ. Optimum superimposition of protein structures: ambiguities and implications. Fold Des. 1996;1:123–132.
  • Fawcett T. An introduction to ROC analysis Pattern Recognition Letters. 2006;27:861–874.
  • Davis J, Goadrich M. The relationship between precision-recall and ROC curves. In: Cohen W, Moore A, editors. Proc. ICML’06: Proceedings of the 23rd international conference on Machine learning; 2006 June 25–29; Pittsburgh, PA. New York: ACM; 2006. p. 233–240.